




import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def        __UpperCAmelCase						(							lowerCamelCase_ :     dict  )       ->  tuple:







			"""simple docstring"""





			return (data["data"], data["target"])





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  np.ndarray:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      XGBRegressor(verbosity=0  ,							random_state=42  )
			xgb.fit(lowerCamelCase_  ,							lowerCamelCase_  )
			# Predict target for test data
			SCREAMING_SNAKE_CASE_   : Dict					  =      xgb.predict(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      predictions.reshape(len(lowerCamelCase_  )  ,							1  )
			return predictions





def        __UpperCAmelCase						(							)       ->  None:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      fetch_california_housing()
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Any					  =      data_handling(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      train_test_split(
			    lowerCamelCase_  ,							lowerCamelCase_  ,							test_size=0.2_5  ,							random_state=1  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      xgboost(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			# Error printing
			print(F'Mean Absolute Error : {mean_absolute_error(lowerCamelCase_  ,							lowerCamelCase_  )}'  )
			print(F'Mean Square Error  : {mean_squared_error(lowerCamelCase_  ,							lowerCamelCase_  )}'  )


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)
						main()











"""simple docstring"""





import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


_SCREAMING_SNAKE_CASE   :							int          =		re.compile(R'''\s+''')







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  return {"hash": hashlib.mda(re.sub(_lowerCamelCase						,							''						,							example['content']					).encode('utf-8'					)					).hexdigest()}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple					) ->							int:
  lowerCamelCase_       	=		[len(_lowerCamelCase					) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_lowerCamelCase					), "line_max": max(_lowerCamelCase					)}







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[str]					) ->							int:
  lowerCamelCase_       	=		np.mean([c.isalnum() for c in example['content']]					)
  return {"alpha_frac": alpha_frac}







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						Optional[int]					) ->							Optional[Any]:
  if example["hash"] in uniques:
    uniques.remove(example['hash']					)
    return True
  else:
    return False







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Optional[Any]=5					) ->							int:
  lowerCamelCase_       	=		['auto-generated', 'autogenerated', 'automatically generated']
  lowerCamelCase_       	=		example['content'].splitlines()
  for _, line in zip(range(_lowerCamelCase					)						,							_lowerCamelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Dict=5						,							_lowerCamelCase			:						List[str]=0.05					) ->							Tuple:
  lowerCamelCase_       	=		['unit tests', 'test file', 'configuration file']
  lowerCamelCase_       	=		example['content'].splitlines()
  lowerCamelCase_       	=		0
  lowerCamelCase_       	=		0
  # first test
  for _, line in zip(range(_lowerCamelCase					)						,							_lowerCamelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  lowerCamelCase_       	=		example['content'].count('\n'					)
  lowerCamelCase_       	=		int(coeff * nlines					)
  for line in lines:
    count_config += line.lower().count('config'					)
    count_test += line.lower().count('test'					)
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any					) ->							List[str]:
  lowerCamelCase_       	=		['def ', 'class ', 'for ', 'while ']
  lowerCamelCase_       	=		example['content'].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Dict=4					) ->							Optional[Any]:
  lowerCamelCase_       	=		example['content'].splitlines()
  lowerCamelCase_       	=		0
  for line in lines:
    counter += line.lower().count('='					)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							List[str]:
  lowerCamelCase_       	=		tokenizer(example['content']						,							truncation=_lowerCamelCase					)['input_ids']
  lowerCamelCase_       	=		len(example['content']					) / len(_lowerCamelCase					)
  return {"ratio": ratio}







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]					) ->							List[Any]:
  lowerCamelCase_       	=		{}
  results.update(get_hash(_lowerCamelCase					)					)
  results.update(line_stats(_lowerCamelCase					)					)
  results.update(alpha_stats(_lowerCamelCase					)					)
  results.update(char_token_ratio(_lowerCamelCase					)					)
  results.update(is_autogenerated(_lowerCamelCase					)					)
  results.update(is_config_or_test(_lowerCamelCase					)					)
  results.update(has_no_keywords(_lowerCamelCase					)					)
  results.update(has_few_assignments(_lowerCamelCase					)					)
  return results







def 	lowerCamelCase__			(				_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Optional[Any]					) ->							Any:
  if not check_uniques(_lowerCamelCase						,							_lowerCamelCase					):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True







def 	lowerCamelCase__			(				_lowerCamelCase			:						str					) ->							int:
  with open(_lowerCamelCase						,							'rb'					) as f_in:
    with gzip.open(str(_lowerCamelCase					) + '.gz'						,							'wb'						,							compresslevel=6					) as f_out:
      shutil.copyfileobj(_lowerCamelCase						,							_lowerCamelCase					)
  os.unlink(_lowerCamelCase					)


# Settings
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		HfArgumentParser(PreprocessingArguments)
_SCREAMING_SNAKE_CASE   :							Any          =		parser.parse_args()
if args.num_workers is None:
  _SCREAMING_SNAKE_CASE   :							List[str]          =		multiprocessing.cpu_count()
_SCREAMING_SNAKE_CASE   :							str          =		AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
_SCREAMING_SNAKE_CASE   :							int          =		load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
_SCREAMING_SNAKE_CASE   :							Tuple          =		ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
_SCREAMING_SNAKE_CASE   :							List[Any]          =		set(ds.unique('''hash'''))
_SCREAMING_SNAKE_CASE   :							Optional[int]          =		len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
_SCREAMING_SNAKE_CASE   :							Dict          =		time.time()
_SCREAMING_SNAKE_CASE   :							Tuple          =		ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
  _SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		deduplicate_dataset(ds_filter, args.jaccard_threshold)
  print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
  print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
_SCREAMING_SNAKE_CASE   :							Dict          =		Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
  with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
    json.dump(duplicate_clusters, f)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
  _SCREAMING_SNAKE_CASE   :							Dict          =		str(data_dir / F'''file-{file_number+1:012}.json''')
  _SCREAMING_SNAKE_CASE   :							Any          =		min(len(ds_filter), index + args.samples_per_file)
  ds_filter.select(list(range(index, end_index))).to_json(file_path)
  compress_file(file_path)






print(F'''Time to save dataset: {time.time()-t_start:.2f}''')




